Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors

Wang, Lingle; Deng, Yu; Knight, Jennifer K.; Wu, Yujie; Kim, Byungchan; Sherman, Woody; Shelley, John C.; Teng, Lin; Abel, Robert

doi:10.1021/ct300911a

Cited by 194 publications

(312 citation statements)

References 44 publications

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“…Both LigandFEP and FEP+ use the same Desmond relaxation protocol and the FEP/REST methodology. 28,41,51,52 By utilizing LigandFEP, we demonstrate LigandFEP can be a powerful tool for academics as it strictly differs only in the level of automation.…”

Section: Methodsmentioning

confidence: 99%

Sensitivity in Binding Free Energies Due to Protein Reorganization

Lim¹,

Wang

Abel

et al. 2016

J. Chem. Theory Comput.

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108

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Tremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand binding free energy calculations to be routinely used to aid pharmaceutical drug discovery projects. However, despite these recent innovations, there are still needs for further improvement in sampling algorithms to more adequately sample protein motion relevant to protein-ligand binding. Here, we report our work identifying and studying such clear and remaining needs in the apolar cavity of T4 Lysozyme L99A. In this study, we model recent experimental results that show the progressive opening of the binding pocket in response to a series of homologous ligands.1 Even while using enhanced sampling techniques, we demonstrate that the predicted relative binding free energies (RBFE) are sensitive to the initial protein conformational state. Particularly, we highlight the importance of sufficient sampling of protein conformational changes and demonstrate how inclusion of three key protein residues in the ‘hot’ region of the FEP/REST simulation improves the sampling and resolves this sensitivity.

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Section: Methodsmentioning

confidence: 99%

Sensitivity in Binding Free Energies Due to Protein Reorganization

Lim¹,

Wang

Abel

et al. 2016

J. Chem. Theory Comput.

Self Cite

108

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“…Sampling is enhanced to some extent in Desmond by allowing periodic replica exchange moves along the alchemical λ -coordinate, which is a protocol known as λ -hopping. It has been shown that sampling in FEP simulations may be enhanced still further by scaling the potential energy of a selected subsystem by a factor less than one at intermediate λ -windows using the replica exchange with solute tempering (REST) procedure [7,20-22]. In REST, since the overall effect of the scaling is to increase the rate of transitions across free energy barriers, the selected subsystem is often referred to as the “hot” region.…”

Section: Computational Detailsmentioning

confidence: 99%

“…The hot region is determined automatically as the group of the ligand that is being mutated. Although it is possible to extend the hot region to include more of the ligand and the receptor [22], by limiting the hot region to a single group, very high effective temperatures can be employed while maintaining good exchange rates between replicas. Effective temperatures were selected automatically with the aim of achieving 30% exchange rates between λ -windows.…”

Section: Computational Detailsmentioning

confidence: 99%

“…This allows the heating of local regions of the system, thus substantially improving traversal of the free energy surface while sampling rigorously from the Boltzmann ensemble. The REST algorithm has been shown to improve agreement with experiment in typical medicinal chemistry projects [8,22]. Both codes use OPLS force fields [23,24], though there are differences in the parameters and the assignment of partial charges [11,12,25,26].…”

Section: Introductionmentioning

confidence: 99%

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Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design

Cole

Tirado‐Rives

Jorgensen

2015

Biochimica et Biophysica Acta (BBA) - General Subjects

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Background Non-nucleoside inhibitors of HIV reverse transcriptase are an important component of treatment against HIV infection. Novel inhibitors are sought that increase potency against variants that contain the Tyr181Cys mutation. Methods Molecular dynamics based free energy perturbation simulations have been run to study factors that contribute to protein–ligand binding, and the results are compared with those from previous Monte Carlo based simulations and activity data. Results Predictions of protein–ligand binding modes are very consistent for the two simulation methods, which are attributed to the use of an enhanced sampling protocol. The Tyr181Cys binding pocket supports large, hydrophobic substituents, which is in good agreement with experiment. Conclusions Although some discrepancies exist between the results of the two simulation methods and experiment, free energy perturbation simulations can be used to rapidly test small molecules for gains in binding affinity. General significance Free energy perturbation methods show promise in providing fast, reliable and accurate data that can be used to complement experiment in lead optimization projects. This article is part of a Special Issue entitled “Recent developments of molecular dynamics”.

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“…25 Therefore, the search for advanced sampling approaches that are effective, relatively economical to use under aqueous conditions, and readily implemented in mainstream molecular simulation software packages is a subject of active development. Here, we have used replica-exchange with solute tempering (REST) MD [32][33][34][35] , as it meets these criteria, and the outcomes from REST compare favourably with temperature-based REMD benchmarks for peptide-materials simulations 25 .…”

Section: Introductionmentioning

confidence: 99%

What makes a good graphene-binding peptide? Adsorption of amino acids and peptides at aqueous graphene interfaces

2015

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Investigation of the non-covalent interaction of biomolecules with aqueous graphene interfaces is a rapidly expanding area. However, reliable exploitation of these interfaces in many applications requires that the links between the sequence and binding of the adsorbed peptide structures be clearly established. Molecular dynamics (MD) simulations can play a key role in elucidating the conformational ensemble of peptides adsorbed at graphene interfaces, helping to elucidate these rules in partnership with experimental characterisation. We apply our recently-developed polarisable force-field for biomolecule-graphene interfaces, GRAPPA, in partnership with advanced simulation approaches, to probe the adsorption behaviour of peptides at aqueous graphene. First we determine the free energy of adsorption of all twenty naturally occurring amino acids (AAs) via metadynamics simulations, providing a benchmark for interpreting peptide-graphene adsorption studies. From these free energies, we find that strong-binding amino acids have flat and/or compact side chain groups, and we relate this behaviour to the interfacial solvent structuring. Second, we apply replica exchange with solute tempering simulations to efficiently and widely sample the conformational ensemble of two experimentally-characterised peptide sequences, P1 and its alanine mutant P1A3, in solution and adsorbed on graphene. For P1 we find a significant minority of the conformational ensemble possesses a helical structure, both in solution and when adsorbed, while P1A3 features mostly extended, random-coil conformations. In solution this helical P1 configuration is stabilised through favourable intra-peptide interactions, while the adsorbed structure is stabilised via interaction of four strongly-binding residues, identified from our metadynamics simulations, with the aqueous graphene interface. Our findings rationalise the performance of the P1 sequence as a known graphene binder.

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Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors

Cited by 194 publications

References 44 publications

Sensitivity in Binding Free Energies Due to Protein Reorganization

Sensitivity in Binding Free Energies Due to Protein Reorganization

Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design

What makes a good graphene-binding peptide? Adsorption of amino acids and peptides at aqueous graphene interfaces

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